This thesis introduces a versatile simulation environment for autonomous racing vehicles, developed at the TUM Autonomous Vehicle System Lab. Initially created for F1Tenth cars, the simulator is adaptable for any vehicle type, including full-scale models. It feature a C++ API and a custom Unity executable for realistic graphics and sensor simulations. A ROS2 extension package is included for easy software-in-the-loop testing, while a Python wrapper implements a Gymnasium environment for machine learning algorithms. The project not only addresses the limitations of the current F1Tenth gym simulator, which is confined to 2D environments and lacks capabilities to include camera and 3D LiDAR sensors, but overcome the lack of open source autonomous racing simulators for vehicles.